from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 2.089471 | 0.179977 | NaN | 0.000383 | 0.002089 | brute | -1 | 1 | 0.663 | 0.177449 | 0.002861 | 0.687 | 11.775048 | 11.776578 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 2.932399 | 0.052733 | NaN | 0.000273 | 0.002932 | brute | -1 | 5 | 0.757 | 0.177626 | 0.002778 | 0.742 | 16.508855 | 16.510874 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 2.060044 | 0.005000 | NaN | 0.000388 | 0.002060 | brute | 1 | 100 | 0.882 | 0.216629 | 0.003587 | 0.875 | 9.509542 | 9.510845 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.021647 | 0.000338 | NaN | 0.000037 | 0.021647 | brute | 1 | 100 | 1.000 | 0.008676 | 0.000137 | 0.000 | 2.495138 | 2.495448 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 2.983173 | 0.040908 | NaN | 0.000268 | 0.002983 | brute | -1 | 100 | 0.882 | 0.213105 | 0.003011 | 0.875 | 13.998639 | 14.000037 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.026092 | 0.003960 | NaN | 0.000031 | 0.026092 | brute | -1 | 100 | 1.000 | 0.009111 | 0.001724 | 0.000 | 2.863663 | 2.914446 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 2.052457 | 0.007466 | NaN | 0.000390 | 0.002052 | brute | 1 | 5 | 0.757 | 0.177995 | 0.001188 | 0.742 | 11.530993 | 11.531249 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.194637 | 0.011474 | NaN | 0.000670 | 0.001195 | brute | 1 | 1 | 0.663 | 0.176718 | 0.001473 | 0.687 | 6.760124 | 6.760359 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.865892 | 0.016249 | NaN | 0.000009 | 0.001866 | brute | -1 | 1 | 0.896 | 0.026653 | 0.000271 | 0.967 | 70.007915 | 70.011521 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.716382 | 0.017446 | NaN | 0.000006 | 0.002716 | brute | -1 | 5 | 0.922 | 0.028074 | 0.000364 | 0.974 | 96.757420 | 96.765533 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 1.946333 | 0.004572 | NaN | 0.000008 | 0.001946 | brute | 1 | 100 | 0.929 | 0.063052 | 0.001949 | 0.975 | 30.868775 | 30.883518 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.893426 | 0.068410 | NaN | 0.000006 | 0.002893 | brute | -1 | 100 | 0.929 | 0.062901 | 0.002982 | 0.975 | 45.999459 | 46.051116 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 1.932452 | 0.005308 | NaN | 0.000008 | 0.001932 | brute | 1 | 5 | 0.922 | 0.028430 | 0.000342 | 0.974 | 67.971399 | 67.976310 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 1.058217 | 0.004668 | NaN | 0.000015 | 0.001058 | brute | 1 | 1 | 0.896 | 0.027144 | 0.000440 | 0.967 | 38.985523 | 38.990654 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.001 | 6.860 | 0.0 | -1 | 1 | 0.048 | 0.003 | 0.242 | 0.243 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.828 | 0.0 | -1 | 5 | 0.047 | 0.000 | 0.249 | 0.249 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.827 | 0.0 | 1 | 100 | 0.047 | 0.000 | 0.251 | 0.251 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.892 | 0.0 | -1 | 100 | 0.047 | 0.000 | 0.247 | 0.247 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 6.970 | 0.0 | 1 | 5 | 0.047 | 0.000 | 0.245 | 0.245 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.018 | 0.0 | 1 | 1 | 0.050 | 0.006 | 0.230 | 0.232 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.374 | 0.0 | -1 | 1 | 0.009 | 0.000 | 0.497 | 0.497 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.375 | 0.0 | -1 | 5 | 0.009 | 0.000 | 0.497 | 0.497 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.378 | 0.0 | 1 | 100 | 0.008 | 0.000 | 0.505 | 0.505 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.377 | 0.0 | -1 | 100 | 0.008 | 0.000 | 0.507 | 0.507 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.357 | 0.0 | 1 | 5 | 0.009 | 0.000 | 0.525 | 0.525 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.373 | 0.0 | 1 | 1 | 0.009 | 0.000 | 0.496 | 0.496 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.089 | 0.180 | 0.000 | 0.002 | -1 | 1 | 0.177 | 0.003 | 11.775 | 11.777 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.025 | 0.002 | 0.000 | 0.025 | -1 | 1 | 0.009 | 0.000 | 2.840 | 2.841 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.932 | 0.053 | 0.000 | 0.003 | -1 | 5 | 0.178 | 0.003 | 16.509 | 16.511 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.025 | 0.002 | 0.000 | 0.025 | -1 | 5 | 0.009 | 0.000 | 2.875 | 2.875 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.060 | 0.005 | 0.000 | 0.002 | 1 | 100 | 0.217 | 0.004 | 9.510 | 9.511 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.022 | 0.000 | 0.000 | 0.022 | 1 | 100 | 0.009 | 0.000 | 2.495 | 2.495 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.983 | 0.041 | 0.000 | 0.003 | -1 | 100 | 0.213 | 0.003 | 13.999 | 14.000 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.026 | 0.004 | 0.000 | 0.026 | -1 | 100 | 0.009 | 0.002 | 2.864 | 2.914 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.052 | 0.007 | 0.000 | 0.002 | 1 | 5 | 0.178 | 0.001 | 11.531 | 11.531 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 5 | 0.009 | 0.000 | 2.350 | 2.350 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.195 | 0.011 | 0.001 | 0.001 | 1 | 1 | 0.177 | 0.001 | 6.760 | 6.760 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.020 | 0.002 | 0.000 | 0.020 | 1 | 1 | 0.009 | 0.000 | 2.214 | 2.214 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.866 | 0.016 | 0.000 | 0.002 | -1 | 1 | 0.027 | 0.000 | 70.008 | 70.012 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.005 | 0.000 | 0.006 | -1 | 1 | 0.001 | 0.000 | 9.212 | 9.285 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.716 | 0.017 | 0.000 | 0.003 | -1 | 5 | 0.028 | 0.000 | 96.757 | 96.766 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.009 | 0.002 | 0.000 | 0.009 | -1 | 5 | 0.001 | 0.000 | 12.795 | 12.919 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.946 | 0.005 | 0.000 | 0.002 | 1 | 100 | 0.063 | 0.002 | 30.869 | 30.884 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.998 | 4.021 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.893 | 0.068 | 0.000 | 0.003 | -1 | 100 | 0.063 | 0.003 | 45.999 | 46.051 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.009 | 0.003 | 0.000 | 0.009 | -1 | 100 | 0.001 | 0.000 | 12.051 | 12.132 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.932 | 0.005 | 0.000 | 0.002 | 1 | 5 | 0.028 | 0.000 | 67.971 | 67.976 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.120 | 4.142 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.058 | 0.005 | 0.000 | 0.001 | 1 | 1 | 0.027 | 0.000 | 38.986 | 38.991 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.622 | 2.638 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.844469 | 0.976965 | NaN | 0.000095 | 0.000844 | kd_tree | -1 | 1 | 0.929 | 0.116938 | 0.002629 | 0.910 | 7.221501 | 7.223326 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 1.026248 | 0.294924 | NaN | 0.000078 | 0.001026 | kd_tree | -1 | 5 | 0.946 | 0.215462 | 0.010701 | 0.941 | 4.763016 | 4.768886 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 6.051497 | 0.298986 | NaN | 0.000013 | 0.006051 | kd_tree | 1 | 100 | 0.951 | 0.635232 | 0.007550 | 0.940 | 9.526433 | 9.527105 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 3.220000 | 0.165930 | NaN | 0.000025 | 0.003220 | kd_tree | -1 | 100 | 0.951 | 0.629795 | 0.008164 | 0.940 | 5.112778 | 5.113208 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.731796 | 0.218939 | NaN | 0.000046 | 0.001732 | kd_tree | 1 | 5 | 0.946 | 0.219942 | 0.002896 | 0.941 | 7.873867 | 7.874549 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 0.940384 | 0.253248 | NaN | 0.000085 | 0.000940 | kd_tree | 1 | 1 | 0.929 | 0.119233 | 0.001555 | 0.910 | 7.886956 | 7.887627 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.024905 | 0.011997 | NaN | 0.000642 | 0.000025 | kd_tree | -1 | 1 | 0.891 | 0.000461 | 0.000106 | 0.879 | 54.046726 | 55.446886 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.022914 | 0.000642 | NaN | 0.000698 | 0.000023 | kd_tree | -1 | 5 | 0.911 | 0.000632 | 0.000022 | 0.905 | 36.237670 | 36.260426 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.032740 | 0.000160 | NaN | 0.000489 | 0.000033 | kd_tree | 1 | 100 | 0.894 | 0.004484 | 0.000024 | 0.917 | 7.301651 | 7.301757 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.035401 | 0.004854 | NaN | 0.000452 | 0.000035 | kd_tree | -1 | 100 | 0.894 | 0.005187 | 0.001434 | 0.917 | 6.824418 | 7.080241 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.019906 | 0.000118 | NaN | 0.000804 | 0.000020 | kd_tree | 1 | 5 | 0.911 | 0.000632 | 0.000019 | 0.905 | 31.498341 | 31.511919 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.018534 | 0.000095 | NaN | 0.000863 | 0.000019 | kd_tree | 1 | 1 | 0.891 | 0.000381 | 0.000025 | 0.879 | 48.622914 | 48.731180 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.520 | 0.075 | 0.023 | 0.0 | -1 | 1 | 0.774 | 0.027 | 4.548 | 4.551 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.918 | 0.057 | 0.020 | 0.0 | -1 | 5 | 0.768 | 0.018 | 5.105 | 5.106 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.848 | 0.097 | 0.021 | 0.0 | 1 | 100 | 0.743 | 0.007 | 5.177 | 5.177 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 4.423 | 0.076 | 0.018 | 0.0 | -1 | 100 | 0.764 | 0.017 | 5.788 | 5.790 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 4.274 | 0.043 | 0.019 | 0.0 | 1 | 5 | 0.760 | 0.018 | 5.622 | 5.624 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.853 | 0.057 | 0.021 | 0.0 | 1 | 1 | 0.780 | 0.011 | 4.942 | 4.942 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.001 | 0.021 | 0.0 | -1 | 1 | 0.003 | 0.002 | 0.229 | 0.257 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | -1 | 5 | 0.002 | 0.002 | 0.241 | 0.298 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | 1 | 100 | 0.001 | 0.001 | 0.459 | 0.542 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.633 | 0.634 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.641 | 0.642 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.639 | 0.639 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.844 | 0.977 | 0.000 | 0.001 | -1 | 1 | 0.117 | 0.003 | 7.222 | 7.223 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.969 | 10.490 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.026 | 0.295 | 0.000 | 0.001 | -1 | 5 | 0.215 | 0.011 | 4.763 | 4.769 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 7.523 | 7.978 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 6.051 | 0.299 | 0.000 | 0.006 | 1 | 100 | 0.635 | 0.008 | 9.526 | 9.527 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | 1 | 100 | 0.001 | 0.000 | 6.411 | 6.841 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.220 | 0.166 | 0.000 | 0.003 | -1 | 100 | 0.630 | 0.008 | 5.113 | 5.113 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 6.489 | 6.919 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.732 | 0.219 | 0.000 | 0.002 | 1 | 5 | 0.220 | 0.003 | 7.874 | 7.875 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 3.603 | 3.879 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.940 | 0.253 | 0.000 | 0.001 | 1 | 1 | 0.119 | 0.002 | 7.887 | 7.888 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.762 | 4.022 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.025 | 0.012 | 0.001 | 0.000 | -1 | 1 | 0.000 | 0.000 | 54.047 | 55.447 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 23.760 | 25.595 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.023 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 36.238 | 36.260 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 23.027 | 24.388 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.033 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.004 | 0.000 | 7.302 | 7.302 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 5.972 | 6.243 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.035 | 0.005 | 0.000 | 0.000 | -1 | 100 | 0.005 | 0.001 | 6.824 | 7.080 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 20.358 | 22.164 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.020 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 31.498 | 31.512 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 6.730 | 7.129 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.019 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 48.623 | 48.731 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 6.749 | 7.117 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.518 | 0.068 | 30 | 0.031 | 0.0 | random | 0.379 | 0.025 | 1.364 | 1.367 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.563 | 0.014 | 30 | 0.028 | 0.0 | k-means++ | 0.408 | 0.025 | 1.380 | 1.382 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.655 | 0.192 | 30 | 0.141 | 0.0 | random | 2.610 | 0.024 | 2.166 | 2.166 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.908 | 0.029 | 30 | 0.135 | 0.0 | k-means++ | 2.751 | 0.010 | 2.147 | 2.148 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.011 | 0.000 | random | 0.0 | 0.0 | 10.996 | 12.760 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 30 | 0.000 | 0.001 | random | 0.0 | 0.0 | 8.501 | 14.001 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.010 | 0.000 | k-means++ | 0.0 | 0.0 | 12.100 | 14.117 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 30 | 0.000 | 0.001 | k-means++ | 0.0 | 0.0 | 13.440 | 14.412 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.467 | 0.000 | random | 0.0 | 0.0 | 7.215 | 7.776 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | random | 0.0 | 0.0 | 13.412 | 13.927 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.486 | 0.000 | k-means++ | 0.0 | 0.0 | 6.841 | 7.436 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | k-means++ | 0.0 | 0.0 | 13.611 | 13.900 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.001739 | 0.000131 | 20 | 0.009203 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000416 | 0.000037 | -0.000965 | 4.181872 | 4.198247 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.001763 | 0.000150 | 20 | 0.009077 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000435 | 0.000052 | -0.000750 | 4.047999 | 4.077106 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002409 | 0.000244 | 20 | 0.332044 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.000953 | 0.000072 | 0.293767 | 2.529253 | 2.536369 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002428 | 0.000230 | 20 | 0.329499 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.000949 | 0.000090 | 0.256968 | 2.559753 | 2.571223 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.074 | 0.000 | 20 | 0.002 | 0.0 | random | 0.026 | 0.001 | 2.831 | 2.835 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.207 | 0.001 | 20 | 0.001 | 0.0 | k-means++ | 0.081 | 0.000 | 2.559 | 2.559 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.198 | 0.012 | 20 | 0.040 | 0.0 | random | 0.106 | 0.001 | 1.856 | 1.856 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 0.560 | 0.008 | 20 | 0.014 | 0.0 | k-means++ | 0.296 | 0.003 | 1.894 | 1.894 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.009 | 0.000 | random | 0.000 | 0.0 | 4.182 | 4.198 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | random | 0.000 | 0.0 | 13.039 | 13.373 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.009 | 0.000 | k-means++ | 0.000 | 0.0 | 4.048 | 4.077 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | k-means++ | 0.000 | 0.0 | 12.301 | 12.670 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.002 | 0.0 | 20 | 0.332 | 0.000 | random | 0.001 | 0.0 | 2.529 | 2.536 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.001 | 0.0 | 20 | 0.001 | 0.001 | random | 0.000 | 0.0 | 9.975 | 10.204 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.002 | 0.0 | 20 | 0.329 | 0.000 | k-means++ | 0.001 | 0.0 | 2.560 | 2.571 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.001 | 0.0 | 20 | 0.001 | 0.001 | k-means++ | 0.000 | 0.0 | 10.935 | 11.297 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000370 | 0.000425 | [20] | 2.163715 | 3.697344e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.000697 | 0.001145 | 0.55 | 0.530832 | 1.021726 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.001694 | 0.000324 | [26] | 4.722577 | 1.693990e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.003262 | 0.001312 | 0.28 | 0.519353 | 0.559785 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 11.072 | 0.365 | [20] | 0.072 | 0.000 | 2.011 | 0.034 | 5.507 | 5.507 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 0.983 | 0.596 | [26] | 0.081 | 0.001 | 0.992 | 0.034 | 0.990 | 0.991 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | [20] | 2.164 | 0.0 | 0.001 | 0.001 | 0.531 | 1.022 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | [20] | 0.017 | 0.0 | 0.000 | 0.000 | 0.361 | 0.366 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | [26] | 4.723 | 0.0 | 0.003 | 0.001 | 0.519 | 0.560 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | [26] | 1.071 | 0.0 | 0.001 | 0.000 | 0.120 | 0.120 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.011618 | 0.000181 | NaN | 6.886163 | 0.000012 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.018498 | 0.000304 | 0.122191 | 0.628034 | 0.628119 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.182 | 0.003 | 0.439 | 0.0 | 0.193 | 0.001 | 0.942 | 0.942 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.130 | 0.074 | 0.708 | 0.0 | 0.308 | 0.265 | 3.672 | 4.844 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.012 | 0.0 | 6.886 | 0.0 | 0.018 | 0.0 | 0.628 | 0.628 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 1.411 | 0.0 | 0.000 | 0.0 | 0.594 | 0.646 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 6.067 | 0.0 | 0.000 | 0.0 | 0.457 | 0.683 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.017 | 0.0 | 0.000 | 0.0 | 0.582 | 0.613 | See | See |